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0Journal of Sea Research 198 (2024) 102472 Journal of Sea Research
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0Dataset collection
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RegionCoordinates
0Tangier36.250206152534847 - 5.977397294819712
1Agadir31.14608750019331 - 9.66901533235119
2Casablanca33.07723556735074 - 7.843945740762594
3Kariat Arkmane35.1203952992372 - 2.734379316487201
0123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303304305306307308309310311312313314315316317318319320321322323324325326327328329330331332333334335336337338339340341342343344345346347348349350351352353354355356357358359360361362363364365366367368369370371372373374375376377378379380381382383384385386387388389390391392393394395396397398399400401402403404405406407408409410411412413414415416417418419420421422423424425426427428429430431432433434435436437438439440441442443444445446447448449450451452453454455456457458459460461462463464465466467468469470471472473474475476477478479480481482483484485486487488489490491492493494495496497498499500501502503504505506507508509510511512513514515516517518519520521522523524525526527528529530531532533534535536537538539540541542543544545546547548549550551552553554555556557558559560561562563564565566567568569570571572573574575576577578579580581582583584585586587588589590591592593594595596597598599600601602603604605606607608609610611612613614615616617618619620621622623624625626627628629630631632633634635636637638639640641642643644645646647648649650651652653654655656657658659660661662663664665666667668669670671672673674675676677678679680681682683684685686687688689690691692693694695696697698699
ParameterExplanation
0x iThe input vector at time t (i.e., SST time series)
1f iThe output of the first layer
2h iThe output of the input gate
3c iThe vector of candidate values that should be added to the cell state c i
4a iThe output of the output state
5h kThe hidden state at time t
6y kThe final predicted output at time t
01
0HyperparameterValue
1Input shape(3593, 60, 1)
2Units128-128-64
3Batch size32
4Learning rate10 - 4
5OptimizerAdam
6Epochs150
7Loss functionMean Squared Error
8Dropout0.2
9Kernel initializerGlorot uniform
ModelNumber of BILSTM layersNumber of dropout layers
0Model A30
1Model B21
2Model C11
3Model D32
ModelMAEMAPERMSER 2TrainingInference
0XGBoost0.31040.017150.44420.954140.110.01
1RF0.31540.017460.454350.9520214.090.01
2SVR0.2980.016490.424580.95811.40.01
3LSTM0.30050.016710.431930.9557497.141.48
4BiLSTM0.29820.016480.42080.95795487.341.45
5Attention-BiGRU0.29950.016550.424330.95724357.571.75
6Transformers0.33880.018950.46490.94868387.791.47
7Attention-BiLSTM0.30950.01720.422220.95767453.722.03
012
0N. Zrina et al.N. Zrina et al.N. Zrina et al.
1Predicting results of Attention-BiLSTM on Tangier.Predicting results of Attention-BiLSTM on Tangier.Predicting results of Attention-BiLSTM on Tangier.
2Predicting results of Attention-BiLSTM on Kariat Arkmane.Predicting results of Attention-BiLSTM on Kariat Arkmane.Predicting results of Attention-BiLSTM on Kariat Arkmane.
3Predicting results of Attention-BiLSTM on Casablanca.Predicting results of Attention-BiLSTM on Casablanca.Predicting results of Attention-BiLSTM on Casablanca.
0
0N. Zrira et al.
1extension to all coastal cities considerably increases the complexity of tuning hyperparameters in deep learning models. Additionally, climate change emerges as a complex and influential factor impacting predictive models due to changes in global or regional weather patterns. The prediction of our model is very close to reality because it is currently
2learmed on stable data. One of the most obvious impacts of climate change is increased SST. The oceans will absorb much of the excess heat trapped by greenhouse gases, causing SST to increase over time. In this case, our model will not be able to predict future SST values well.
3In future work, we plan to explore the application of the neural prophet model to Moroccan SST data as well as other marine datasets such as Pirata (the Prediction and Research Moored Array in the Atlantic). By testing the neural prophet model, we aim to further enhance our understanding and predictive capabilities in the domain of SST forecasting. This will contribute to the advancement of marine
4Credit authorship contribution statement
5Nabila Zrira: Writing - review & editing. Writing - original draft. Methodology. Investigation. Data curation. Conceptualization. Assia Kamal-Idrissi: Writing - original draft, Validation, Methodology, Investigation. Formal analysis. Conceptualization. Rahma Farssi: Writing - review & editing. Writing - original draft, Conceptualization. Haris Ahmad Khan: Writing - review & editing. Writing - original
01
0N. Zrina et al.Journal of Sea Research 198 (2024) 103472
12016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1480-1489.Zheng, Gang, Li, Xiaofeng, Zhang, Rong-Hui, Zhang, Bin, 2020. Purely satellite data-driven deep learning forecast of complicated spatial instability waves, Sci. Adv. 6 (29)
2Zhang, Xiaoyu, Li, Yongqing, Freery, Alejandro C., Ren, Peng, 2021. Sea surface temperature prediction with memory graph convolutional networks. IEEE Geosci. Remote Sens. Lett. 19 (0), 1-5.eaba1482.